CN111417300A - Field measurements of soil element concentrations - Google Patents
Field measurements of soil element concentrations Download PDFInfo
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Abstract
In one embodiment, a system for measuring in real time the concentration of soil elements in a field is disclosed. The system includes a retrieval device coupled to a mobility component configured to move the system in an agricultural field. The extraction device is configured to continuously receive a plurality of soil samples from a soil probe coupled to the mobility assembly while the mobility assembly is operating. The extraction device contains an extractant solution that is a solvent for the soil sample. Further, the extraction device includes a mixer configured to mix the soil sample with the extractant solution, thereby forming a solution mixture. The system also includes a chemical sensor coupled to the extraction device, the chemical sensor configured to measure a concentration level of a soil element in the solution mixture. Additionally, the system includes a processor coupled to the chemical sensor, the processor configured to calculate a concentration level of a soil element in each of the plurality of soil samples after the extraction device receives the soil sample and before the extraction device receives the successive soil samples.
Description
Copyright notice
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the patent and trademark office patent file or records, but otherwise reserves all copyright rights whatsoever.
2015-2018 Climate (Clemitt Corp.).
Technical Field
The present disclosure relates to soil content measurement, and in particular to real-time measurement of soil element concentrations in a field.
Background
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Thus, unless otherwise indicated, any methods described in this section should not be assumed to be prior art merely by virtue of their inclusion in this section.
Growers benefit from healthy soil. Maintaining healthy soil often includes accurately tracking the amount of nutrients (such as nitrate) in the soil. The amount of nitrate or another type of nutrient in the soil may vary with location within the field, sampling time, environmental conditions, or physical characteristics of the soil, with soil type, humidity, and temperature, among other things, having significant effects.
One common method for measuring concentration levels of particular soil elements includes collecting a soil sample from a field, sending the soil sample to a laboratory, and receiving concentration level measurements from the laboratory after days or even weeks. However, due to the various factors mentioned above, the concentration levels may change rapidly over time. For example, during transport from field to laboratory, the amount of nitrate is expected to decrease by several times. Thus, measurements obtained from a laboratory may not accurately reflect the concentration levels of a particular soil element at the time the soil sample was taken.
Disclosure of Invention
The appended claims may be used as the summary of the disclosure.
Drawings
In the drawings:
fig. 1 illustrates an example computer system configured to perform the functions described herein, shown in a field environment with other devices with which the system may interoperate.
FIG. 2 illustrates two views of an example logical organization of a set of instructions in main memory when an example mobile application program is loaded for execution.
FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using agronomic data provided by one or more data sources.
FIG. 4 is a block diagram that illustrates a computer system upon which an embodiment of the invention may be implemented.
FIG. 5 depicts an example embodiment of a timeline view of data entry.
FIG. 6 depicts an example embodiment of a spreadsheet view of data entry.
Fig. 7 illustrates an example process performed by the mobile soil analysis system to collect and analyze soil samples in a field.
FIG. 8 illustrates an example mobile soil analysis system.
FIG. 9 shows an example extraction device and chemical sensor.
10A-10C illustrate the conversion of data showing cumulative concentration levels in a solution mixture to concentration levels in individual soil samples.
Fig. 11 illustrates an example process performed by a processor (such as an application or equipment controller) to control a mobile soil analysis system to determine a concentration of a soil element in a soil sample in real-time.
Detailed Description
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present disclosure. Embodiments are disclosed in various parts according to the following summary:
1. general overview
2. Example agricultural Intelligent computer System
2.1. Brief description of the construction
2.2. Application overview
2.3. Data ingestion for computer systems
2.4. Process overview-agronomic model training
2.5. Soil analysis
2.6. Implementation example-hardware overview
3. Mobile soil analysis system
3.1 System overview
3.2 real-time soil extraction, sensing and measurement Unit
*
1. General overview
A mobile soil analysis system for measuring in real time the concentration of soil elements in a field is disclosed. Applications of the system include autumn soil nutrient analysis as well as soil chemical analysis at the time of sowing (early or late spring) and during the growing season. In one embodiment, the system includes a device that can receive successive soil samples and measure in real time the concentration level of a target soil element (such as nitrate or nitrogen) in each soil sample. The apparatus may include: a cartridge for containing an extractant solution, an automatic mixer for mixing soil samples into the extractant solution, a selective chemical sensor for measuring a concentration level of a target soil element in the mixture, and a processor for calculating the concentration level of the target soil element in each soil sample from the sensor data. The system may further comprise: a mobility component that allows the system to be applied across fields; a soil detector for collecting successive soil samples as the system travels; and a position sensor for tracking soil to collect position or other position data. The system may complete the process from collecting a soil sample to determining the concentration level of the target soil element in a few seconds and may immediately repeat the process. Given the location data, expected production levels, or other additional data, the processor may also be configured to generate recommendations for adjusting the concentration levels of soil target elements in real time.
In one embodiment, a real-time-the-go ("OTG") analysis method for direct measurement of nitrate (nitrogen) in soil is disclosed, consisting of: a box-like device containing an extraction liquid; an automatic mixer; and a selective sensor for measuring target chemical nutrients (i.e., nitrate/nitrogen) in a sequential manner across the field. The data system for capturing the GPS coordinates and measured nitrate from where the sample(s) were taken is linked to an automatic calculator software which, along with the input of the detected amount of nitrate/nitrogen and GPS coordinates from where the samples were taken, will allow triggering of the specific application of fertilizer required for planting a specific crop. In one embodiment, such OTG devices and methods allow for rapid estimation of the amount of soil nitrate (nitrogen) while traversing the field. The output data can be used to subsequently tailor a site-specific fertilizer/nitrate (nitrogen) application plan for the crop of interest to the farmer. The whole system comprises: a moving vehicle for carrying and transporting the entire apparatus across a field; an automated soil detector for collecting target samples from a field at a defined soil depth; a box-like apparatus having a specified volume of one or more extractant solutions; selective chemical sensors for nitrates, nitrogen or other compounds or elements and for rapid measurement of specific soil chemical nutrients; a GPS system for capturing the location of the sample; and a computer having control software for controlling sample collection, measured target analytes, storing data, and calculating fertilizer application rates for the field being analyzed.
In one embodiment, the removable box-like device is located in a moving vehicle for dynamic measurement of soil nitrate as it is collected. In one embodiment, the box-like apparatus has an opening for introducing a specified volume of a target soil sample, for introducing a sensor, and for having an automatic mixer. In addition, the cartridge device may contain a specified volume of an extractant solution that is capable of rapidly mixing with the soil and dissolving the target analyte once it has been introduced and measured by the selective nitrate/nitrogen sensor. To analyze several soil samples, the cartridge will be constructed with a specified size (e.g., gallon capacity) that can hold a continuous number of soil samples in the same volume of extractant solution. The box-like apparatus can be mounted in a vehicle while being moved across a field. Further, the above system would be coupled to a computer and software system to operate the method, collect measurement data and capture the GPS coordinates where each of the sample(s) was taken. Such a system would provide as an output the amount of nutrients present in the sampled field area, and this output could trigger an alarm that signals whether to simultaneously add a specified amount of fertilizer to compensate for the target amount of nutrients needed to plant a particular crop with a defined target yield.
In one embodiment, a soil measurement system includes: a moving vehicle for loading and transporting the box-like apparatus across the field; one or more soil detectors for collecting target samples from the field at defined soil depths; a box-like apparatus having a specified volume of one or more extractant solutions; selective chemical sensors (e.g., nitrate/nitrogen sensors) for rapid measurement of specific soil chemical compositions; a GPS for capturing the sampled location; and a computer with custom software for controlling the overall operation (sample collection, target analyte measurement and stored data).
In some embodiments, the mobile soil analysis system enables a grower to accurately and densely analyze soil nutrient changes under many field conditions. In particular, the system can assist growers in managing fertilization decisions in real-time to achieve a desired crop yield response. For example, the system may assist the grower in determining the appropriate time, number, or location (i.e., field area) to fertilize or not fertilize, or in selecting a particular crop seed hybrid/variety that will produce the best yield under the farmer's field conditions and nutrient measurements. The system may also facilitate the research and development of new crop varieties with enhanced fertilizer use properties by providing real-time measurements of soil element concentrations. In addition, the system can assist users in real-time monitoring of fields that are highly vulnerable to chemical contamination to address soil nutrient loss, thereby contributing to current sustainable environmental practices.
Embodiments provide the benefit of helping farmers to sustainably increase productivity by applying fertilizer in the correct amount at the correct place. Embodiments may provide benefits that allow for verification that current fertilization programs will supply sufficient nitrate fertility for the current year of crop and to determine how much supplemental nitrogen is needed. In addition, since some fields do not respond to nitrogen, performing OTG testing would be a way to screen out those fields. OTG systems may allow for field measurements after rainy seasons and/or for determination of nitrate carry-over after drought. For the examples, it would also be possible to examine fields with different crop rotation and fertilizer application histories.
Embodiments may provide particular benefits of a dynamic nitrate measurement system for accurately and precisely quantifying heterogeneous soil with nitrate levels of 5-25ppm, field resolution of one sample per 10 feet, and sampling depths of 6-12 inches.
2. Example agricultural Intelligent computer System
2.1 structural overview
Fig. 1 illustrates an example computer system configured to perform the functions described herein, shown in a field environment with other devices with which the system may interoperate. In one embodiment, the user 102 owns, operates, or possesses a field manager computing device 104 in or associated with a field location (such as a field for agricultural activities or a management location for one or more fields). The field manager computer device 104 is programmed or configured to provide field data 106 to the agricultural intelligence computer system 130 via one or more networks 109.
Examples of field data 106 include (a) identification data (e.g., area, field name, field identifier, geographic identifier, boundary identifier, crop identifier, and any other suitable data that may be used to identify a field such as public land unit (C L U), plot and block number, lot number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, zone number, field number, area, land area, and/or range) (b) harvest data (e.g., crop type, crop variety, crop rotation, whether the crop was planted organically, harvest date, Actual Production History (APH), expected yield, crop price, crop income, grain moisture, cultivation practice, and previous growing season information) (C) soil data (e.g., type, composition, pH, Organic Matter (OM), Cation Exchange Capacity (CEC)), and (d) planting data (e.g., planting date, type of seed(s) planted, relative degree of seed(s) (RM), seed density(s), soil density (CEC), and other plant growth parameters such as a weather forecast, weather rate, weather conditions, weather conditions, soil moisture, soil characteristics, soil.
The data server computer 108 is communicatively coupled to the agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to the agricultural intelligence computer system 130 via one or more networks 109. The external data server computer 108 may be owned or operated by the same legal or entity as the agricultural intelligent computer system 130, or by a different person or entity, such as a governmental agency, non-governmental organization (NGO), and/or private data service provider. Examples of external data include weather data, image data, soil data, or statistics related to crop yield, etc. The external data 110 may be comprised of the same type of information as the field data 106. In some embodiments, the external data 110 is provided by an external data server 108 owned by the same entity that owns and/or operates the agricultural intelligence computer system 130. For example, agricultural intelligence computer system 130 can include a data server dedicated to data types (such as weather data) that might otherwise be obtained from third party sources. In some embodiments, the external data server 108 may actually be incorporated within the system 130.
Agricultural device 111 may have one or more remote sensors 112 fixed thereon that are communicatively coupled to agricultural intelligence computer system 130 directly or indirectly via agricultural device 111 and programmed or configured to transmit sensor data to agricultural intelligence computer system 130 examples of agricultural device 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, aircraft including unmanned aerial vehicles, and any other physical machinery or hardware items, typically mobile machinery, and may be used for tasks associated with agriculture in some embodiments, a single unit of device 111 may include a plurality of sensors 112 locally coupled in a network on the device, a Controller Area Network (CAN) is an example of such a network that may be installed in combines, harvesters, sprayers, and cultivators, application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via network(s) 109 and programmed or configured to receive from agricultural intelligence computer system 130 one or more sensors 112 for controlling operating parameters or implementation of agricultural vehicles or, for example, a Controller Area Network (CAN) script may be used to implement remote sensors 112 from agricultural intelligence computer system 130, and may be used in embodiments, for example, remote field communication from agricultural intelligence computer system 111, agricultural intelligence Corporation, factory, field automation.
The apparatus 111 may include a cab computer 115 programmed with a cab application that may include a version or variation of a mobile application for the device 104 described further elsewhere herein. In one embodiment, cab computer 115 comprises a compact computer, typically a tablet-sized computer or smartphone, having a graphical screen display, such as a color display, mounted within the cab of the operator of device 111. The cab computer 115 may implement some or all of the operations and functions described further herein with respect to the mobile computer device 104.
The network (S) 109 broadly represents any combination of one or more data communication networks, including local area networks, wide area networks, the Internet, or the Internet, using any of wired or wireless links, including terrestrial or satellite links, the one or more networks may be implemented by any medium or mechanism that provides for the exchange of data between the various elements of FIG. 1 may also have direct (wired or wireless) communication links the sensors 112, controller 114, external data server computer 108, and other elements of the system each include interfaces compatible with the one or more networks 109, and are programmed or configured to use standardized protocols for communication across the networks, such as TCP/IP, Bluetooth, CAN protocols, and higher layer protocols (such as HTTP, T L S, etc.).
The agricultural intelligence computer system 130 is programmed or configured to receive field data 106 from the field manager computing device 104, external data 110 from the external data server computer 108, and sensor data from the remote sensors 112. The agricultural intelligence computer system 130 may be further configured to host, use, or execute one or more computer programs, other software elements, digitally programmed logic (such as FPGAs or ASICs), or any combination thereof, to perform the transformation and storage of data values, the construction of digital models of one or more crops on one or more fields, the generation of recommendations and notifications, and the generation and transmission of scripts to the application controller 114 in the manner described further in other sections of this disclosure.
In one embodiment, agricultural intelligence computer system 130 is programmed with or includes a communication layer 132, a presentation layer 134, a data management layer 140, a hardware/virtualization layer 150, and a model and field data store 160. A "layer" in this context refers to any combination of electronic digital interface circuitry, a microcontroller, firmware (such as drivers), and/or computer programs or other software elements.
The communication layer 132 may be programmed or configured to perform input/output interface functions including sending requests for field data, external data, and sensor data to the field manager computing device 104, the external data server computer 108, and the remote sensors 112, respectively. The communication layer 132 may be programmed or configured to send the received data to the model and field data repository 160 for storage as field data 106.
The presentation layer 134 may be programmed or configured to generate a Graphical User Interface (GUI) to be displayed on the field manager computing device 104, the cab computer 115, or other computer coupled to the system 130 via the network 109. The GUI may include controls for inputting data to be sent to the agricultural intelligence computer system 130, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.
As used herein, a database may include any collection of data (including hierarchical databases, relational databases, flat file databases, object relational databases, object-oriented databases, distributed databases), and any other structured collection of records or data stored in a computer system
MYSQL、
DB2、
SQL SERVER、
And a POSTGRESQ L database however, any database that implements the systems and methods described herein may be used.
The user 102 may specify the identification data by accessing a map on the user device (served by the agricultural intelligent computer system) and drawing the boundaries of the fields on the map in an alternative embodiment, the user 102 may specify the identification data by accessing a map on the user device (served by the agricultural intelligent computer system 130) and drawing the boundaries of the fields on the map.
In an example embodiment, agricultural intelligence computer system 130 is programmed to generate and cause display of a graphical user interface that includes a data manager for data entry. After one or more fields have been identified using the above-described methods, the data manager can provide one or more graphical user interface widgets that, when selected, can identify changes in the fields, soil, crops, farming, or nutrient practices. The data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs.
FIG. 5 depicts an example embodiment of a timeline view of data entry. Using the display depicted in fig. 5, the user computer may input a selection of a particular field and a particular date to add an event. Events depicted at the top of the timeline may include nitrogen, planting, practice, and soil. To add a nitrogen administration event, the user computer may provide input to select a nitrogen tag. The user computer may then select a location on the timeline for a particular field to indicate application of nitrogen on the selected field. In response to receiving a selection of a location of a particular field on the timeline, the data manager may display a data entry overlay allowing the user computer to enter data related to nitrogen application, planting processes, soil application, farming processes, irrigation practices, or other information related to the particular field. For example, if the user computer selects a portion of the timeline and indicates the application of nitrogen, the data entry overlay may include fields for entering the amount of nitrogen applied, the date of application, the type of fertilizer used, and any other information related to the application of nitrogen.
In one embodiment, the data manager provides an interface for creating one or more programs. A "program" in this context refers to a set of data relating to nitrogen application, planting processes, soil application, farming processes, irrigation practices, or other information that may be associated with one or more fields and may be stored in a digital data storage device for reuse as a set in other operations. After a program has been created, it can be conceptually applied to one or more fields, and references to the program can be stored in digital storage in association with data identifying those fields. Thus, instead of manually entering the same data relating to the same nitrogen application for a plurality of different fields, the user computer may create a program that indicates a particular application of nitrogen and then apply that program to a plurality of different fields. For example, in the timeline view of FIG. 5, the first two timelines have selected the "spring application" program, which includes 150lbs N/ac applied at the beginning of 4 months. The data manager may provide an interface for editing the program. In one embodiment, when a particular program is edited, each field that has selected the particular program is edited. For example, in fig. 5, if the "spring application" program was edited to reduce the application of nitrogen to 130lbs N/ac, the first two fields may be updated with reduced nitrogen application based on the edited program.
In one embodiment, in response to receiving an edit to a field for which a program was selected, the data manager de-associates the field with the selected program. For example, if nitrogen application is added to the top field in fig. 5, the interface may be updated to indicate that the "spring application" program is no longer being applied to the top field. While nitrogen administration at the beginning of 4 months may remain, renewal of the "spring application" program will not alter nitrogen administration at 4 months.
FIG. 6 depicts an example embodiment of a spreadsheet view for data entry. Using the display depicted in fig. 6, a user can create and edit information for one or more fields. The data manager may include a spreadsheet as depicted in fig. 6 for entering information about nitrogen, planting, practice, and soil. To edit a particular entry, the user computer may select a particular entry in the spreadsheet and update a value. For example, fig. 6 depicts an ongoing update to the target yield values for the second field. Further, the user computer may select one or more fields for application of one or more programs. In response to receiving a program selection for a particular field, the data manager can automatically complete an entry for the particular field based on the selected program. As with the timeline view, the data manager may update the entries for each field associated with a particular program in response to receiving an update to that program. Further, the data manager can, in response to receiving an edit to one of the entries for a field, release the selected program from correspondence with the field.
In one embodiment, the model and field data is stored in a model and field data repository 160. The model data includes a data model created for one or more fields. For example, the crop model may include a digitally constructed model of the development of crops on one or more fields. A "model" in this context refers to an electronic digital storage collection of executable instructions and data values associated with one another that are capable of receiving and responding to programmatic or other digital calls, or resolution requests based on specified input values to produce one or more stored or calculated output values that may be used as a basis for computer-implemented recommendations, output data displays, or machine control, etc. Those skilled in the art find it convenient to express a model using mathematical equations, but this form of expression does not limit the model disclosed herein to abstract concepts; rather, each model herein has practical application in computers in the form of stored executable instructions and data that use computers to implement the model. The model may include a model of past events over one or more fields, a model of a current state of one or more fields, and/or a model of predicted events over one or more fields. The model and field data may be stored in data structures in memory, in rows in database tables, in flat files or spreadsheets, or in other forms of stored digital data.
In some embodiments, agricultural intelligence computer system 130 is programmed with a soil analysis server ("server") 170 or includes server 170. The server 170 is further configured to include a soil element concentration analysis component 172 and a client interface 174. Each of the soil element concentration analysis component 172 and the client interface 174 may be implemented as a sequence of stored program instructions. In some embodiments, soil element concentration analysis component 172 is programmed to receive input data from one or more sources and output a current concentration level of a target analyte in the soil or a recommendation for adjusting the current concentration level. The input data to the soil element concentration analysis component 172 may include data generated by a mobile soil analysis system introduced above and discussed further in fig. 8, which may include one or more of the agricultural apparatus 111, the application controller 114, and the remote sensor 112. An example of such data would be the current nitrate concentration levels in certain soil samples. Additional input data may include data received from user computers (such as field manager computing device 104 or cab computer 115) or from data server computer 108, or other data already stored in model data field data store 160, such as expected crop yield levels, soil nutrient loss history, historical weather reports or weather forecasts, or records of application of other types of soil nutrients. The output data from the soil element concentration analysis component 172 may include when and how concentration levels of certain soil nutrients or other elements are adjusted and where such adjustments should be applied. Such data may be transmitted to the user's computer or other remote computer.
In some embodiments, client interface 174 is configured to manage communications with a mobile soil analysis system or user computer over a communications network through communications layer 132. The communication may include receiving instructions to begin real-time field measurements and desired soil conditions or production levels from the user computer, sending instructions to the mobile soil analysis system to perform real-time measurements of soil element concentration levels, receiving soil measurements from the mobile soil analysis system, and sending results of analyzing the soil measurements with respect to the desired soil conditions or production levels to the user computer.
Each component of server 170 comprises a set of one or more pages of main memory (such as RAM) in agricultural intelligence computer system 130 into which executable instructions have been loaded and which, when executed, cause the agricultural intelligence computer system to perform the functions or operations described herein with reference to those modules. For example, soil element concentration analysis component 172 may include a set of pages in RAM that contain instructions that when executed result in performing the soil element concentration analysis described herein. These instructions may be in machine-executable code in the CPU's instruction set, and may have been compiled based on source code written in JAVA, C + +, OBJECTIVE-C, or any other human-readable programming language or environment, alone or in combination with scripts, other scripting languages, and other programming source text in JAVASCRIPT. The term "page" is intended to broadly refer to any area within main memory, and the specific terminology used in the system may vary depending on the memory architecture or the processor architecture. In another embodiment, each component of server 170 may also represent one or more source code files or items digitally stored in a mass storage device, such as non-volatile RAM or disk storage, in agricultural intelligence computer system 130 or a separate repository system, which when compiled or interpreted results in the generation of executable instructions that, when executed, cause the agricultural intelligence computer system to perform the functions or operations described herein with reference to those modules. In other words, the figure may represent the manner in which a programmer or software developer organizes and arranges the source code for later compilation into an executable file or interpretation into bytecode or equivalent for execution by agricultural intelligence computer system 130.
The hardware/virtualization layer 150 includes one or more Central Processing Units (CPUs), memory controllers, and other devices, components, or elements of a computer system, such as volatile or non-volatile memory, non-volatile storage (such as disks), and I/O devices or interfaces, such as those illustrated and described in connection with fig. 4. Layer 150 may also include programming instructions configured to support virtualization, containerization, or other techniques.
For purposes of illustrating a clear example, fig. 1 shows a limited number of examples of certain functional elements. However, in other embodiments, any number of such elements may be present. For example, embodiments may use thousands or millions of different mobile computing devices 104 associated with different users. Additionally, the system 130 and/or the external data server computer 108 may be implemented using two or more processors, cores, clusters, or instances of physical or virtual machines configured in discrete locations or co-located with other elements in a data center, shared computing facility, or cloud computing facility.
2.2. Application overview
In one embodiment, the functions described herein are implemented using one or more computer programs or other software elements loaded into and executed using one or more general-purpose computers, which will be configured as specific machines or as computers specifically configured to perform the functions described herein. Additionally, each of the flow diagrams further described herein may be used alone or in combination with the descriptions of procedures and functions herein as an algorithm, a plan, or an orientation that can be used to program a computer or logic to perform the described functions. In other words, all text and all figures herein are intended to provide a disclosure of an algorithm, plan or direction sufficient to allow a skilled person, in conjunction with the skill and knowledge of a person having a skill level appropriate for such disclosure and disclosure, to program a computer to perform the functions described herein.
In one embodiment, the user 102 interacts with the agricultural intelligence computer system 130 using a field manager computing device 104 configured with an operating system and one or more applications or applications; the field manager computing device 104 may also independently and automatically interoperate with the agricultural intelligence computer system under program control or logic control, and direct user interaction is not always required. The field manager computing device 104 broadly represents one or more of the following: a smart phone, a PDA, a tablet computing device, a laptop computer, a desktop computer, a workstation, or any other computing device capable of sending and receiving information and performing the functions described herein. The field manager computing device 104 can communicate via a network using a mobile application stored on the field manager computing device 104, and in some embodiments, the device can be coupled to the sensors 112 and/or the controller 114 using cables 113 or connectors. A particular user 102 may own, operate, or have and use more than one field manager computing device 104 in conjunction with the system 130 at a time.
The field manager computing device 104 may send and receive data to and from one or more front-end servers using a network-based protocol or format (such as HTTP, XM L, and/or JSON) or an application-specific protocol.
In one embodiment, the field manager computing device 104 transmits field data 106 to the agricultural intelligence computer system 130, the field data 106 containing or including, but not limited to, data values representing one or more of geographic locations of one or more fields, farming information for one or more fields, crops planted in one or more fields, and soil data extracted from one or more fields the field manager computing device 104 may transmit the field data 106 in response to user input from the user 102 specifying data values for one or more fields, further, the field manager computing device 104 may automatically transmit the field data 106 when one or more of the data values become available to the field manager computing device 104. for example, the field manager computing device 104 may be communicatively coupled to the remote sensor 112 and/or application controller 114, the remote sensor 112 and/or application controller 114 including an irrigation sensor and/or irrigation controller 35may communicate, in response to receipt of HTTP messages indicating that the application controller 114 released water onto one or more fields, the field manager computing device may transmit data 106 in response to the field manager computing device using an interactive field communication protocol, the field manager computing device may transmit data values indicating that the field data was released by the agricultural intelligence computing device 106 in response to the field manager computing device 104 transmitting an interactive communication between the field data transmission or field data transmission of the field manager computing device 130.
A commercial example of a mobile application is the C L IMAGE FIE L DVIEW.C L IMAGE FIE L DVIEW application or other application, available from Claimett, Inc. of san Francisco, Calif., can be modified, extended, or adapted to include features, functions, and programming not disclosed prior to the filing date of this disclosure.
FIG. 2 illustrates two views of an example logical organization of a set of instructions in main memory when an example mobile application program is loaded for execution. In FIG. 2, each named element represents an area of one or more pages of RAM or other main memory, or an area of one or more blocks of disk storage or other non-volatile storage, and programming instructions within those areas. In one embodiment, in view (a), the mobile computer application 200 includes account-field-data ingest-share instructions 202, summary and alert instructions 204, digital map book instructions 206, seed and plant instructions 208, nitrogen instructions 210, weather instructions 212, field health instructions 214, and performance instructions 216.
In one embodiment, the mobile computer application 200 includes account, field, data ingestion, sharing instructions 202 programmed to receive, convert, and ingest field data from third party systems via manual upload or APIs. The data types may include field boundaries, yield maps, planting maps, soil test results, application maps, and/or management areas, among others. The data format may include a shape file, a third party's native data format, and/or a Farm Management Information System (FMIS) export, and the like. Receiving data may occur via: manual upload, email with attachments, an external API to push data to the mobile application, or an instruction to call an API of an external system to pull data into the mobile application. In one embodiment, mobile computer application 200 includes a data inbox. In response to receiving a selection of a data inbox, the mobile computer application 200 may display a graphical user interface for manually uploading data files and importing the uploaded files to the data manager.
In one embodiment, the digital map book instructions 206 include a field map data layer stored in device memory and programmed with a data visualization tool and geospatial field annotations. This provides the grower with a close to touch convenient message for him to refer to, record and observe the field performance. In one embodiment, the summary and alert instructions 204 are programmed to provide a view of the operating range of what is important to the grower, as well as timely suggestions for action or focus on a particular problem. This allows the grower to focus time on places where attention is needed to save time and maintain production throughout the season. In one embodiment, the seed and plant instructions 208 are programmed to provide tools for seed selection, hybrid placement, and script creation (including Variable Rate (VR) script creation) based on scientific models and empirical data. This enables the grower to maximize yield or return on investment through optimized seed purchase, placement and density.
In one embodiment, the script generation instructions 205 are programmed to provide an interface for generating scripts, including Variable Rate (VR) fertility scripts. The interface enables the grower to create scripts for field practices such as nutrient application, planting, and irrigation. For example, the planting script interface may include tools for identifying the type of seed for planting. Upon receiving a selection of a seed type, the mobile computer application 200 may display one or more fields divided into a management area, such as a field map data layer created as part of the digital map book instructions 206. In one embodiment, the management area includes soil areas and a panel that identifies each soil area and the soil name, texture, drainage or other field data for each area. Mobile computer application 200 may also display tools for editing or creating, such as graphical tools for drawing a management area (such as a soil area), on a map of one or more fields. The planting process may be applied to all of the management areas, or different planting processes may be applied to different subsets of the management areas. When the script is created, the mobile computer application 200 can make the script available for download in a format readable by the application controller (such as an archived or compressed format). Additionally, and/or alternatively, scripts may be sent directly from mobile computer application 200 to cab computer 115 and/or uploaded to one or more data servers and stored for further use.
In one embodiment, nitrogen instructions 210 are programmed to provide a tool to inform nitrogen decisions by visualizing the availability of nitrogen to crops. This enables the grower to maximize yield or return on investment by optimizing nitrogen application during the season. Example programming functions include: displaying an image (such as an SSURGO image) to enable the fertilizer application area and/or an image generated from sub-field soil data (such as data obtained from sensors) to be rendered with high spatial resolution (fine to millimeters or less depending on proximity and resolution of the sensors); uploading an existing grower-defined area; providing a map of plant nutrient availability and/or a map enabling the regulation of nitrogen application across multiple areas; outputting the script to drive the mechanical device; tools for mass data entry and adjustment; and/or maps for data visualization, etc. "mass data input" in this context may mean that the data is input once and then the same data is applied to a plurality of fields and/or regions that have been defined in the system; example data may include nitrogen application data that is the same for many fields and/or areas of the same grower, but such large data inputs are suitable for inputting any type of field data into the mobile computer application 200. For example, nitrogen instructions 210 may be programmed to accept definitions of nitrogen application and practice programs and to accept user input specifying that those programs be applied across multiple fields. A "nitrogen administration program" in this context refers to a stored named data set associated with: name, color code or other identifier, one or more application dates, material or product type and quantity per date, method of application or incorporation (such as injection or broadcast), and/or amount or rate of application per date, crop or hybrid being the subject of application, and the like. A "nitrogen practice program" in this context refers to a stored named data set associated with: a practice name; a previous crop; a farming system; date of primary farming; one or more previous farming systems that were used; one or more indicators of the type of application used (such as fertilization). Nitrogen instructions 210 may also be programmed to generate and cause display of a nitrogen map indicating a prediction of plant usage for a specified nitrogen and whether an excess or a deficiency is predicted; in some embodiments, different color indicators may indicate either an excess magnitude or an insufficient magnitude. In one embodiment, the nitrogen map comprises: a graphical display in a computer display device comprising a plurality of rows, each row associated with and identifying a field; data specifying what crops are planted in the field, the field size, the field location, and a graphical representation of the field perimeter; in each row, a monthly timeline with graphical indicators specifies each nitrogen application and quantity at a point associated with a month name; and a number and/or a colored excess or deficiency indicator, wherein the color indicates the magnitude.
In one embodiment, the nitrogen map may include one or more user input features, such as dials or sliders, for dynamically changing the nitrogen planting and practice program so that the user may optimize his or her nitrogen map. The user may then use their optimized nitrogen map and associated nitrogen planting and practice programs to implement one or more scripts, including a Variable Rate (VR) fertility script. Nitrogen instructions 210 may also be programmed to generate and cause display of a nitrogen map indicating a prediction of plant usage for a specified nitrogen and whether an overage or an under-age is predicted; in some embodiments, different color indicators may indicate either an excess magnitude or an insufficient magnitude. The nitrogen map may use numerical and/or colored surplus or deficit indicators to display predictions of plant usage for a given nitrogen and whether surplus or deficit is predicted for different times in the past and future (such as daily, weekly, monthly or yearly), where the color indicates the magnitude. In one embodiment, the nitrogen map may include one or more user input features, such as dials or sliders, for dynamically changing the nitrogen planting and practice procedure so that the user may optimize his nitrogen map, for example, to obtain a preferred excess to make up for the deficit. The user may then use their optimized nitrogen map and associated nitrogen planting and practice programs to implement one or more scripts, including a Variable Rate (VR) fertility script. In other embodiments, instructions similar to nitrogen instruction 210 may be used for application of other nutrients (such as phosphorus and potassium), application of pesticides, and irrigation programs.
In one embodiment, the weather instructions 212 are programmed to provide field-specific recent weather data and weather forecast information. This enables the grower to save time and have an efficient integrated display of daily operational decisions.
In one embodiment, the field health instructions 214 are programmed to provide timely, remotely sensed images that highlight season crop changes and potential concerns. Example programming functions include: cloud checking to identify possible clouds or cloud shadows; determining a nitrogen index based on the field image; graphical visualization of scout layers, including those relating to field health, for example, and viewing and/or sharing of scout notes; and/or download satellite images from multiple sources and determine image priorities for growers, etc.
In one embodiment, the performance instructions 216 are programmed to provide reporting, analysis, and insight tools using farm data to make assessments, insights, and decisions. This enables growers to seek improved results for the next year through factual conclusions regarding why return on investment is at a previous level and insights into yield limiting factors. Performance instructions 216 may be programmed to be transmitted to a back-end analysis program executing at agricultural intelligence computer system 130 and/or external data server computer 108 via one or more networks 109 and configured to analyze metrics such as yield, yield variation, hybrids, density, SSURGO area, soil test attributes or elevation, and the like. The programmed reporting and analysis may include: yield variability analysis, treatment outcome estimation, benchmarking of yield and other metrics against other growers based on anonymous data collected from many growers or data of seeds and plants, and the like.
An application with instructions configured in this manner may be implemented for different computing device platforms while maintaining the same general user interface appearance. For example, a mobile application program may be programmed to execute on a tablet, smart phone, or server computer that is accessed using a browser at a client computer. Additionally, mobile applications configured for tablet computers or smart phones may provide a full application experience or cab application experience that is tailored to the display and processing capabilities of cab computer 115. For example, referring now to view (b) of fig. 2, in one embodiment, the cab computer application 220 may include map-cab (maps-cap) instructions 222, remote view instructions 224, data collection and transmission instructions 226, machine warning instructions 228, script transmission instructions 230, and reconnaissance-cab instructions 232. The code library of instructions of view (b) may be the same as that of view (a), and the executables implementing the code may be programmed to the type of platform on which they execute, and programmed to disclose through a graphical user interface only those functions applicable to the cab platform or full platform. The method enables the system to identify distinct user experiences applicable to the environment in the cab and the different technical environments of the cab. The map-cab instructions 222 may be programmed to provide map views of fields, farms, or areas that are useful in directing the operation of the machine. Remote view instructions 224 may be programmed to open, manage, and provide a view of real-time or near real-time machine activity to other computing devices connected to system 130 via a wireless network, wired connector or adapter, or the like. The data collection and transmission instructions 226 may be programmed to turn on, manage, and provide for transmission of data collected at the sensors and controllers to the system 130 via a wireless network, wired connector or adapter, or the like. The machine warning instructions 228 may be programmed to detect an operational problem with a machine or tool associated with the cab and generate an operator warning. Script transmission instructions 230 may be configured to be transmitted in the form of instruction scripts configured to direct machine operations or collection of data. The reconnaissance-cab instructions 232 may be programmed to display location-based alerts and information received from the system 130 based on the location of the field manager computing device 104, the agricultural apparatus 111, or the sensor 112 in the field, and to ingest, manage, and provide for transmission of location-based reconnaissance observations to the system 130 based on the location of the agricultural apparatus 111 or the sensor 112 in the field.
2.3. Data ingestion for computer systems
In one embodiment, the external data server computer 108 stores external data 110 including soil data representing soil composition of one or more fields and weather data representing temperature and precipitation of one or more fields. The weather data may include past and current weather data and predictions of future weather data. In one embodiment, the external data server computer 108 includes multiple servers hosted by different entities. For example, a first server may contain soil composition data and a second server may include weather data. Further, the soil composition data may be stored in a plurality of servers. For example, one server may store data representing the percentage of sand, silt and clay in the soil, while a second server may store data representing the percentage of Organic Matter (OM) in the soil.
In one embodiment, remote sensors 112 include one or more sensors programmed or configured to generate one or more observations. Remote sensors 112 may be aerial sensors (such as satellites), vehicle sensors, planting equipment sensors, farming sensors, fertilizer or pesticide application sensors, harvester sensors, and any other implement capable of receiving data from one or more fields. In one embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. The application controller 114 may also be programmed or configured to control operating parameters of the agricultural vehicle or implement. For example, the application controller may be programmed or configured to control operating parameters of a vehicle (such as a tractor), planting equipment, farming equipment, fertilizer or pesticide equipment, harvester equipment, or other farm implements (such as water valves). Other embodiments may use any combination of sensors and controllers, of which the following are merely selected examples.
The system 130 may obtain or ingest data from a large number of growers that have contributed data to a shared database system on a large scale under the control of the users 102 when one or more user-controlled computer operations are requested or triggered to obtain data for use by the system 130, this form of obtaining data may be referred to as "manual data ingesting". for example, a C L image FIE L DVIEW application, available from Clarmett, Inc., of san Francisco, Calif., may be operated to export data to the system 130 for storage in the repository 160.
For example, the seed monitor system CAN both control the planter device components and obtain planting data, including signals from the seed sensors via a signal harness that includes a CAN backbone and point-to-point connections for registration and/or diagnostics. The seed monitor system may be programmed or configured to display seed spacing, density, and other information to a user via the cab computer 115 or other device within the system 130. Examples are disclosed in U.S. patent No.8,738,243 and U.S. patent publication No. 20150094916, and this disclosure assumes knowledge of those other patent publications.
Likewise, the yield monitor system may include a yield sensor for the harvester device that sends yield measurement data to the cab computer 115 or other equipment within the system 130. The yield monitor system may utilize one or more remote sensors 112 to obtain grain moisture measurements in the combine or other harvester and send these measurements to the user via the cab computer 115 or other devices within the system 130.
In one embodiment, examples of sensors 112 that may be used with any moving vehicle or device of the type described elsewhere herein include kinematic sensors and position sensors. The kinematic sensors may include any speed sensor, such as a radar or wheel speed sensor, an accelerometer, or a gyroscope. The location sensor may include a GPS receiver or transceiver, or a WiFi-based location or mapping application, or the like, programmed to determine location based on nearby WiFi hotspots.
In one embodiment, examples of sensors 112 that may be used with a tractor or other moving vehicle include: an engine speed sensor, a fuel consumption sensor, an area counter or distance counter interacting with GPS or radar signals, a PTO (power take off) speed sensor, a tractor hydraulic sensor configured to detect hydraulic parameters (such as pressure or flow) and/or hydraulic pump speed, a wheel speed sensor or a wheel slip sensor. In one embodiment, examples of the controller 114 that may be used with a tractor include: a hydraulic directional controller, a pressure controller, and/or a flow controller; a hydraulic pump speed controller; a speed controller or governor; a suspension position controller; or the wheel position controller provides automatic steering.
In one embodiment, examples of sensors 112 that may be used with seed planting devices such as planters, seed drills, or air planters include: a seed sensor, which may be an optical, electromagnetic or collision sensor; down force sensors such as load pins, load cells, pressure sensors; a soil property sensor, such as a reflectance sensor, a humidity sensor, a conductivity sensor, an optical residue sensor, or a temperature sensor; component operation standard sensors, such as a planting depth sensor, a lower pressure cylinder pressure sensor, a seed tray speed sensor, a seed drive motor encoder, a seed conveyor system speed sensor, or a vacuum sensor; or pesticide application sensors such as optical or other electromagnetic sensors or impact sensors. In one embodiment, examples of a controller 114 that may be used with such a seed planting device include: a toolbar fold controller, such as a controller for a valve associated with a hydraulic cylinder; a downforce controller, such as a controller for a valve associated with a pneumatic, air bag, or hydraulic cylinder, and programmed to apply downforce to individual row units or the entire planter frame; an implant depth controller, such as a linear actuator; a metering controller, such as an electric seed meter drive motor, a hydraulic seed meter drive motor, or a swath control clutch; a hybrid selection controller, such as a seed meter drive motor, or other actuator programmed to selectively allow or prevent a seed or air seed mixture from delivering seeds into or from a seed meter or central bulk hopper; a metering controller, such as an electric seed meter drive motor or a hydraulic seed meter drive motor; a seed conveyor system controller, such as a controller for a belt seed delivery conveyor motor; a flag controller, such as a controller for a pneumatic or hydraulic actuator; or an insecticide application rate controller such as a metering drive controller, orifice size or position controller.
In one embodiment, examples of sensors 112 that may be used with the tilling apparatus include: a position sensor for a tool such as a handle or a plate; a tool position sensor for such tools configured to detect depth, coaxial angle (gang angle), or lateral spacing; a down force sensor; or a stretch force sensor. In one embodiment, examples of the controller 114 that may be used with the tilling apparatus include a downforce controller or a tool position controller, such as a controller configured to control tool depth, coaxial angle, or lateral spacing.
In one embodiment, examples of sensors 112 that may be used in connection with a device for applying fertilizer, pesticides, fungicides, and the like (such as an activated fertilizer system on a planter, a subsoil applicator, or a fertilizer sprayer) include: a fluid system standard sensor, such as a flow sensor or a pressure sensor; a sensor indicating which of the head valves or fluid line valves are open; a sensor associated with the tank, such as a level sensor; a segmented or full system supply line sensor, or a line-specific supply line sensor; or a kinematic sensor such as an accelerometer disposed on the sprayer boom. In one embodiment, examples of a controller 114 that may be used with such a device include: a pump speed controller; a valve controller programmed to control pressure, flow, direction, PWM, etc.; or a position actuator such as for boom height, subsoiler depth, or boom position.
In one embodiment, examples of sensors 112 that may be used with a harvester include: a yield monitor, such as a shock plate strain gauge or position sensor, a capacitive flow sensor, a load cell, a weight sensor or a torque sensor associated with an elevator or auger (auger), or an optical or other electromagnetic grain height sensor; grain moisture sensors, such as capacitive sensors; grain loss sensors, including crash sensors, optical sensors or capacitive sensors; header operation standard sensors, such as a header height sensor, a header type sensor, a deck gap sensor, a feeder speed sensor, and a reel speed sensor; separator operating standard sensors such as notch plate clearance, rotor speed, shoe clearance, or screen clearance sensors; auger sensors for position, operation or speed; or an engine speed sensor. In one embodiment, examples of a controller 114 that may be used with a harvester include: a header operation standard controller for elements such as header height, header type, deck clearance, feeder speed, or reel speed; a separator operating standard controller for features such as shoe clearance, rotor speed, shoe clearance, or screen clearance; or a controller for auger position, operation or speed.
In one embodiment, examples of sensors 112 that may be used with a grain trailer include weight sensors or sensors for auger position, operation or speed. In one embodiment, examples of the controller 114 that may be used with a grain trailer include a controller for auger position, operation, or speed.
In one embodiment, examples of sensors 112 and controllers 114 may be installed in an Unmanned Aerial Vehicle (UAV) device or "drone. Such sensors may include cameras having detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, Near Infrared (NIR), and the like; an accelerometer; an altimeter; a temperature sensor; a humidity sensor; pitot tube sensors or other airspeed or wind speed sensors; a battery life sensor; or a radar transmitter and a reflected radar energy detection device; other electromagnetic radiation emitters and reflected electromagnetic radiation detection devices. Such a controller may include: a boot or motor control device, a control surface controller, a camera controller, or a controller programmed to turn on, operate, obtain data from, manage, and configure any of the aforementioned sensors. Examples are disclosed in U.S. patent application No. 14/831,165, and the present disclosure assumes knowledge of this other patent disclosure.
In one embodiment, the sensor 112 and controller 114 may be secured to a soil sampling and measurement device configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other soil-related tests. For example, the devices disclosed in U.S. patent No.8,767,194 and U.S. patent No.8,712,148 may be used, and the present disclosure assumes knowledge of those patent disclosures.
In one embodiment, the sensors 112 and the controller 114 may include weather equipment for monitoring weather conditions of the field. For example, the apparatus disclosed in U.S. provisional application No. 62/154,207 filed on 29/4/2015, U.S. provisional application No. 62/175,160 filed on 12/6/2015, U.S. provisional application No. 62/198,060 filed on 28/7/2015, and U.S. provisional application No. 62/220,852 filed on 18/9/2015 may be used, and the present disclosure assumes knowledge of those patent disclosures.
2.4. Process overview-agronomic model training
In one embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in the memory of the agricultural intelligence computer system 130 that includes field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also include calculated agronomic characteristics describing conditions or characteristics of one or more crops or both that may affect the growth of one or more crops on the field. Further, the agronomic model may include recommendations based on agronomic factors, such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvesting recommendations, and other crop management recommendations. Agronomic factors may also be used to estimate one or more crop related outcomes, such as agronomic yield. The agronomic yield of a crop is an estimate of the number of crops produced, or in some examples, the income or profit gained from the crops produced.
In one embodiment, the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic characteristics related to the currently received location and crop information for one or more fields. The preconfigured agronomic model is based on previously processed field data including, but not limited to, identification data, harvest data, fertilizer data, and weather data. The pre-configured agronomic models may have been cross-validated to ensure accuracy of the models. Cross-validation may include comparison to ground truth that compares predicted results to actual results on the field, such as comparing rainfall estimates to rain gauges or sensors that provide weather data at the same or nearby locations, or comparing estimates of nitrogen content to soil sample measurements.
FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. Fig. 3 may be used as an algorithm or instructions for programming the functional elements of agricultural intelligence computer system 130 to perform the operations now described.
At block 305, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise, distortion effects, and confounds within the agronomic data, including measurement outliers that may adversely affect the received field data values. Examples of agronomic data preprocessing may include, but are not limited to: removing data values normally associated with anomalous data values, certain measured data points known to unnecessarily skew other data values, data smoothing, aggregation, or sampling techniques to remove or reduce additive or multiplicative effects from noise, and other filtering or data derivation techniques to provide a clear distinction between positive and negative data inputs.
At block 310, the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the pre-processed field data to identify data sets that are useful for the initial agronomic model generation. The agricultural intelligence computer system 130 can implement data subset selection techniques including, but not limited to, genetic algorithm methods, all subset model methods, sequential search methods, stepwise regression methods, particle swarm optimization methods, and ant colony optimization methods. For example, genetic algorithm selection techniques use adaptive heuristic search algorithms to determine and evaluate data sets within pre-processed agronomic data based on natural selection and evolutionary principles of genetics.
At block 315, the agricultural intelligence computer system 130 is configured or programmed to implement field dataset evaluation. In one embodiment, a particular field data set is evaluated by creating an agronomic model and using a particular quality threshold on the created agronomic model. The agronomic models may be compared and/or validated using one or more comparison techniques, such as, but not limited to, leave-one-cross validation Root Mean Square Error (RMSECV), mean absolute error, and mean percent error. For example, the RMSECV may cross-validate an agronomic model by comparing predicted agronomic attribute values created by the agronomic model with the collected and analyzed historical agronomic attribute values. In one embodiment, agronomic data set evaluation logic is used as a feedback loop, wherein agronomic data sets that do not meet the configured quality threshold are used during the future data subset selection step (block 310).
At block 320, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic model creation based on the cross-validated agronomic data set. In one embodiment, the agronomic model creation may implement multivariate regression techniques to create a preconfigured agronomic data model.
At block 325, the agricultural intelligence computer system 130 is configured or programmed to store the preconfigured agronomic data model for future field data evaluations.
2.5. Implementation example-hardware overview
According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. A special-purpose computing device may be hardwired to perform the techniques, or may include a digital electronic device such as one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs) that is permanently programmed to perform the techniques, or may include one or more general-purpose hardware processors programmed to perform the techniques according to program instructions in firmware, memory, other storage, or a combination. Such special purpose computing devices may also incorporate custom hardwired logic, ASICs, or FPGAs with custom programming to implement these techniques. A special-purpose computing device may be a desktop computer system, portable computer system, handheld device, networked device, or any other device that contains hardwired and/or program logic for implementing the techniques.
For example, FIG. 4 is a block diagram that illustrates a computer system 400 upon which an embodiment of the invention may be implemented. Computer system 400 includes a bus 402 or other communication mechanism for communicating information, and a hardware processor 404 coupled with bus 402 for processing information. Hardware processor 404 may be, for example, a general purpose microprocessor.
As used herein, the term "storage media" refers to any non-transitory media that stores data and/or instructions that cause a machine to function in a particular manner.
A storage medium is different from, but may be used in combination with, a transmission medium. Transmission media participate in the transfer of information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 402. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 404 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 400 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 402. Bus 402 carries the data to main memory 406, from which main memory 406 processor 404 retrieves and executes the instructions. The instructions received by main memory 406 may optionally be stored on storage device 410 either before or after execution by processor 404.
Network link 420 typically provides data communication through one or more networks to other data devices. For example, network link 420 may provide a connection through local network 422 to a host computer 424 or to data equipment operated by an Internet Service Provider (ISP) 426. ISP426 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the "Internet" 428. Local network 422 and internet 428 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 420 and through communication interface 418, which carry the digital data to and from computer system 400, are exemplary forms of transmission media.
The received code may be executed by processor 404 as it is received, and/or stored in storage device 410, or other non-volatile storage for later execution.
3. Mobile soil analysis system
3.1 System overview
Fig. 7 illustrates an example process performed by the mobile soil analysis system to collect and analyze soil samples in a field. In some embodiments, a mobile soil analysis system may traverse a field, repeatedly collecting soil samples along the way and measuring soil element concentrations in real time. The mobile soil analysis system may include a device capable of processing an accumulated plurality of soil samples and measuring in real time a concentration level of a particular soil element in each of the plurality of soil samples. Thus, mobile soil analysis systems may use the device to process up to a certain number of continuously collected soil samples and replace or renew the device from time to time as it traverses the field.
In some embodiments, the mobile soil analysis system is configured to collect soil samples at predetermined locations or times, such as following a predetermined route and collecting periodically. The mobile soil analysis system may travel at different speeds at different times, such as faster between sample collection points but slower around the sample collection points. For example, a mobile soil analysis system may maintain an average speed between 1 and 12 miles per hour and collect soil samples every 10-60 feet.
In some embodiments, for each soil sample, the mobile soil analysis system is configured to perform one or more of the following steps: sample collection 702, soil treatment 704, screening or sorting 706, transportation 708, metering 710, extraction 712, measurement or calibration 714, and waste treatment 716. The steps may be performed in the order indicated, or in another order. Mobile soil analysis system may be configured to additionally perform data collection 718 or upload to cloud 720. As previously mentioned, additional steps may also be performed by the server or the user computer.
In some embodiments, in sample collection 702, the mobile soil analysis system is configured to collect soil samples of a particular size at a particular depth of a field through a soil probe. For example, the soil detector may be a cutter wheel, cutter disc/plow, bucket wheel, or core/cam shaft. The cutting/disc plow assembly may comprise: cutting wheels for breaking up the soil, shovels for guiding the soil into elevators or augers or screws for transporting the soil upwards. Other types of soil detectors known to those skilled in the art may be used. The soil detector may collect and otherwise process soil samples as the mobile soil analysis system moves, but it may be desirable to reduce the speed of movement. The soil sample size may have a range of 60 plus or minus 12 grams and the sampling depth may have a range between 0 and 12 inches.
In some embodiments, in soil treatment 704, the mobile soil analysis system is configured to disintegrate the soil in the soil sample and produce a relatively homogenous soil of the ground surface by the soil grinder. The soil mill may be a roller mill or a rotating blade. Other types of soil mills known to those skilled in the art may be used.
In some embodiments, in screening or sorting 706, the mobile soil analysis system is configured to retain the majority of soil particles through the soil screen and exclude undesirable soil aggregates that may be detrimental to downstream analysis. The soil screen may be made of stainless steel with a diameter of 2 mm. Other types of soil screens known to those skilled in the art may be used.
In some embodiments, in transport 708, the mobile soil analysis system is configured to transport the soil sample through a soil transporter, such as from a soil detector, soil grinder, or soil screen to a soil metering assembly or soil element extractor, as discussed further below. The soil transporter may be an auger screw or a bucket elevator. Other types of soil transporters known to those skilled in the art may be used.
In some embodiments, in metering 710, the mobile soil analysis system is configured to measure various properties of the soil sample, such as its volume, weight, density, or moisture content, by a soil meter. The soil meter may be a volumetric water content sensor, a weight hopper, a gross weight measuring device or an in-line microwave. Other types of soil gauges known to those skilled in the art may be used.
In some embodiments, in extracting 712, the mobile soil analysis system is configured to extract the target soil element from the soil sample via an extraction device. In measurement or calibration 714, the mobile soil analysis system is configured to detect the amount of the target soil element in the extraction device through the chemical sensor. The mobile soil analysis system is also configured to analyze, by the processor, the data generated by the chemical sensor to determine a concentration level of the target soil element in each soil sample provided to the extraction device. In waste treatment 716, the mobile soil analysis system is configured to process waste that may have been generated in an extraction, measurement, or calibration, or another step. The extraction device, chemical sensor, processor and waste treatment will be discussed in detail in the next section.
In some embodiments, the mobile soil analysis system is configured to detect the current location by a location sensor such as GPS. The mobile soil analysis system may also be configured to send data generated by location sensors, chemical sensors, soil meters, or processors to a server in data collection 718 or to upload to cloud 720, where the cloud may include model data field data repository 160 in fig. 1.
FIG. 8 illustrates an example mobile soil analysis system. In some embodiments, mobile soil analysis system 800 may include mobility component 814 for providing mobility. Mobility component 814 may be a vehicle capable of moving while carrying one or more other components of mobile soil analysis system 800. The vehicle may travel on the ground, such as a planter or an all-terrain vehicle ("ATV"), or in the air, such as a drone. The vehicle may be operated via the engine, mechanically or manually.
In some embodiments, mobile soil analysis system 800 may include a soil probe 802 for collecting soil samples from a field and an extraction device 804 for extracting target soil elements from the soil samples. Soil probe 802 may be directly coupled to mobility component 814 and include a soil transporter for transporting soil samples to another component of mobile soil analysis system 800, such as extraction device 804. The soil transporter may also be a separate component that connects two or more components of mobile soil analysis system 800, such as soil prober 802 and extraction device 804. A soil grinder for decomposing soil in the soil sample, a soil screen for selecting desired soil particles from the soil sample, or a soil meter for measuring properties of the soil sample may also be incorporated into the mobile soil analysis system 800 along the path from the soil detector 802 to the extraction device 804.
An example of how to prepare a soil sample for the extraction device 804 is described below. During collection of soil samples, the hydraulic power soil probe 802 may be lowered into the soil through a load-bearing frame that is part of the soil transporter. While moving within a defined travel distance, soil probe 802 may cut a core of soil at a depth, and a soil transporter may transport a portion of the shredded soil produced by soil probe 802 to a central baggy soil sample holder, which may also be a portion of a soil gauge located above extraction device 804. Soil probe 802 may have integrated a soil grinder and soil screen to produce a soil sample with uniform bulk density and fine granular particles that facilitate the subsequent nitrate extraction process. The soil sample may then be subjected to a scraper placed above the bag sample holder, which may be moved to remove excess soil before the soil sample is added to the extraction device 804.
In some embodiments, mobile soil analysis system 800 may include: a chemical sensor 808 for detecting the amount of the target soil element in the extraction device 804, a processor 812 for analyzing data generated by the chemical sensor 808, and a location sensor 816 for detecting the current location. The chemical sensor 808 may be coupled to the extraction device 804 and the processor 812 may then be coupled to the chemical sensor 808. A position sensor 816 may also be coupled to the processor 812. One or more of the chemical sensor 808, the processor 812, and the position sensor 816 may be directly coupled to the mobility assembly 814. A waste disposal mechanism for disposing of waste material generated by the extraction device 804 or another component may also be incorporated into the mobile soil analysis system 800. The extraction device 804, chemical sensor 808, processor 812, and waste treatment will be discussed in detail in the next section.
3.2 real-time soil extraction, sensing and measurement Unit
FIG. 9 shows an example extraction device and chemical sensor. In one embodiment, the extraction device may be implemented using a removable component, referred to as a cartridge, which may be alone or in combination with a chemical sensor, which may be fitted into and removed from the mobile soil analysis system 800 of fig. 8. Using a removable, replaceable cartridge, the mobile soil analysis system 800 can be used to collect soil at several successively different points in a field and place a set of multiple soil samples from multiple points into the cartridge, which is removed prior to collecting the next set of multiple samples. The removable, replaceable nature allows the mobile soil analysis system 800 to operate as a dynamic soil sampling system that can take multiple consecutive in-field samples while traveling using modular convenience components that reduce the amount of time and improve the efficiency of obtaining samples across a distributed field area.
In some embodiments, the extraction device includes a container 924 for holding an extractant solution 910 and a plurality of soil samples. The extractant solution 910 is capable of dissolving a target analyte, such as nitrate, in the soil. The extraction device may have certain openings for receiving soil samples and moving other components of soil analysis system 800. For example, the extraction device can include a lid 912 having one or more openings. The first opening 914 may be used to receive a soil sample. The second opening 916 may be used for insertion of the mixer 906, the mixer 906 being configured to mix the soil sample into an extractant solution (hereinafter "solution mixture") that may have been mixed with one or more soil samples. Instead, the second opening 916 may simply be a junction point when the mixer 906 is integrated into the extraction device. The third opening 918 may be used to insert a chemical sensor 908, the chemical sensor 908 configured to detect an amount of a target analyte in a solution mixture. The mixer 906 or chemical sensor 908 may be coupled to the lid 912, the container 924, or another portion of the mobile soil analysis system using common fastening means such as welding, screws, or adhesives. The lid 912 may be fitted to the container 924 and stay in place when the container 924 is replaced.
In some embodiments, the container 924 may be a stand-alone disposable unit or a static solution tank. The container 924 may be made of a material that can withstand field travel and does not react with the solution mixture, such as plastic. The container may have a circular base to facilitate movement of the solution mixture during mixing. As part of waste disposal, the individual disposable units may be moved aside and eventually removed from mobile soil analysis system 800. The stand alone disposable unit may be replaced with another when the solution mixture has reached saturation, when a certain amount of soil sample has been added to the extractant solution 910 or the solution mixture, when a certain amount of time has elapsed, or when another condition is met. The static solution tank stays in place, but its contents undergo treatment cycles, each beginning with the extractant solution 910 that may need to be poured in, including combining the extractant solution 910 or solution mixture with more soil samples over time, and ending with the solution mixture being purged for reuse or drained as part of waste treatment.
In some embodiments, the extractant solution 910 may comprise water or a dilute salt solution, for example, because substantially all nitrates in the soil with low anion exchange capacity are water soluble. Depending on the type of chemical sensor 908, certain warning measures may be taken with the extractant solution 910. For example, when nitrate is measured as nitrogen NO3-N by ion chromatography or Ion Selective Electrode (ISE), chloride in the extractant solution 910 may interfere with the analysis. In this case, for example, ammonium sulfate (NH4)2SO4 may be a preferred extractant. Alternatively, a selective inhibitor chelator may be added to the non-specific extractant solution 910 to eliminate interference with the target analyte. Some chelators, such as nitrate interference inhibitor solution ("NISS"), are commercially available and may be added to the extractant solution 910 to use ISE nitrate sensors. The use of NISS protects ISE from most of the interferences (i.e. anions such as chloride) present in soil. Other candidate sensors (such as the SupraSensor) typically do not require NISS in the extractant solution 910 because the anions do not interfere with nitrate sensing by the SupraSensor. Furthermore, because the minimum detection limit of ISE for nitrate is about 1.4ppm, a baseline of 2-5ppm of nitrate can be added to the extractant solution 910 to avoid low level measurements in the non-linear region of ISE. Specifically, pure nitrate may be added to the water at the baseline concentration, and the mixture may be added to the extractant solution 910.
In some embodiments, the mixer 906 may be an overhead stirrer that may be inserted down into the container 924 through an opening 916 in the lid 912 and may include a bottom paddle or blade for stirring. The size of these paddles or blades may depend on the size of the vessel 924, the volume of the solution mixture, the desired agitation speed, or other factors. The overhead stirrer may be made of a material (such as steel) that will not deform from the stirring impact and will not interact with the solution mixture. An overhead stirrer may be coupled to a motor coupled to the mobile soil analysis system for automatic stirring at a speed of at least 10rpm to allow nitrate dissolution or extraction to be complete and the solution mixture ready for measurement in one second. The mixer 906 may also be a recirculation pump that continuously stirs the solution mixture by air pressure. The recirculation pump may be configured to have a mixing speed above 10rpm to allow dissolution or extraction to be completed within one second.
In some embodiments, the chemical sensor 908 may include an ISE that enables direct wet soil sensing. For example, nitrate ISE may be used to measure the concentration of nitrate NO3 "in an aqueous sample. The ISE may be a conventional ISE based on a liquid junction (liquid junction) or a modern solid-state ISE based on a solid junction (solid junction). Generally, ISE converts the activity of specific ions dissolved in a solution into an electrical potential. Commercial ISE may include a processing unit that also converts the measured potential for the target analyte to a human-readable concentration level. Such commercial ISEs may have a range of at least 0.1-14,000 ppm, while the expected range of targeted soil elements may be, for example, 0-50 ppm. In addition, such commercial ISE is expected to produce readings within 10 seconds with reproducibility within plus or minus 10% of full scale. Prior to use, the ISE may be calibrated using a prepared reagent grade target analyte standard solution to ensure that the sensor is functioning as intended. For example, the sensor may be inserted into a pure nitrate standard solution (e.g., 10 and 100ppm nitrate in water), a slope may be determined, and then a linear equation may be used to calculate the nitrate response to a defined test sample based on the slope. As part of waste disposal, the ISE may need to be replaced from time to time. Other types of sensors can be used, such as a SupraSensor built using microchip-based technology with a proprietary chemical coating (ChemFET) specifically selected for nitrate.
In some embodiments, the size of the container 924 and the amount of extractant solution 910 may depend on the amount of soil to be dissolved, the amount of target analyte present in the soil, the sensitivity of the chemical sensor 908, or other factors. The amount of soil to be dissolved is in turn related to the frequency of soil sample collection and the size of the soil samples collected. As discussed above, in one ideal scenario, mobile soil analysis system 800 may be moving at an average speed of about 12 miles per hour, and collect soil samples about every 10 feet, meaning that the frequency of soil sample collection will be every 6-10 seconds. Such frequencies may impose constraints on the mixing speed of the mixer 906 and the detection speed of the chemical sensor 908, as discussed further below. Additionally, in an ideal scenario, each soil sample collected may weigh about 60 grams. Based on a maximum of 20 soil samples to be dissolved in the extractant solution 910 and a weight ratio of extractant to soil of 10: 1, the amount of extractant solution 910 can be calculated. The size of the vessel 924 can then be estimated based on the density of the soil and extractant solution 910, the amount of space required to effect mixing, or other factors. The size of the container 924 may also be limited by the capacity of the mobility assembly in the soil analysis system, the capacity of the waste disposal, or other factors. A maximum of 20 soil samples will also require decontamination or replacement of the container 924 approximately every 2-3 minutes. The container volume may also be tailored to meet specific throughput or other requirements. For example, the container volume may be increased to reduce the replacement rate.
In some embodiments, the processor 812 may be coupled with the chemical sensor 808 or specifically with the chemical sensor 908 and receive data from the chemical sensor 808 for further processing, as discussed above. Fig. 10 illustrates the conversion of data showing the cumulative concentration levels in a solution mixture to concentration levels in individual soil samples. Fig. 10A shows an example diagram showing: as 20 soil samples were continuously mixed into the extractant solution, how the nitrate concentration level in the solution mixture changed, as measured by the chemical sensor. Fig. 10A includes a line graph in which the vertical Y-axis indicates nitrate concentration in Parts Per Million (PPM) and the horizontal X-axis indicates time in seconds. In the exemplary graph of fig. 10A, soil samples were added approximately every 100 seconds in this experiment. In other embodiments or experiments, as mentioned above, the collection of the soil sample, the mixing of the soil sample into the extractant solution, and the measurement of the concentration level of the target analyte in the solution mixture are expected to be completed within 6-10 seconds. Each near vertical segment of the curve indicating a rapid increase in nitrate concentration level substantially corresponds to the addition of a soil sample to the extractant solution or solution mixture.
Fig. 10B shows an example graph illustrating the nitrate concentration levels calculated by processor 812 in each of the 20 soil samples that were added sequentially to the solution mixture. Figure 10B includes a bar graph in which the vertical Y-axis specifies the nitrate concentration calculated as PPM and the horizontal X-axis specifies a particular sample of a plurality of samples, such as up to 20 samples in this example. Processor 812 can be programmed to acquire the data shown in fig. 10A and determine the amount of each rapid increase to produce the data shown in fig. 10B. For example, each increase that occurs within the second duration threshold that is greater than the first number threshold may be identified. Although the figure indicates that nitrate concentration levels in each soil sample in this experiment can be as high as about 100ppm, in other examples or experiments nitrate concentration levels are expected to be between 0-50ppm, and chemical sensors are expected to be sensitive enough to detect relatively low concentration levels.
Fig. 10C shows sample statistics related to the experiment. FIG. 10C is a data table identifying nitrate concentration, soil NO measured in the laboratory, with exemplary values for each specified metric3Average NO measured using a cassette such as that shown in FIG. 8 or FIG. 93Standard deviation, percent recovery, accuracy and accuracy. In the example of FIG. 10C, the percent recovery is about 95 and the percent accuracy or relative error is about-5.3. Here, the percentage recovered refers to the amount of nitrate measured by the sensor under the test conditions in the total amount originally present in the soil sample; the amount that is not in solution will become unrecovered nitrate. Furthermore, the percentage (negative value) of relative error generally estimates the accuracy of the assayAnd in this case in particular the amount of unmeasured nitrate in the expected 100% in the soil. These statistics are expected to remain similar if not unchanged in the above-mentioned production settings.
In some embodiments, the processor 812 is configured to perform further analysis and generate suggestions for the user. The processor may also receive additional data from the location sensors, as discussed above, and create a nitrate map for the field that indicates the nitrate concentration level for each unit of the field. The processor may also receive additional data indicative of various factors affecting field health, such as weather reports, fertilization history, target production, moisture indicators, or pollutant updates, and generate feasible recommendations. For example, by determining how effective certain fertilizers can generally be and how much nutrients are currently in a region of the field, the processor may suggest how much fertilizer to apply to the region to achieve a certain crop yield. The processor may then send these suggestions to a central server or user computer.
In some embodiments, the processor may be coupled with the mobility component of the mobile soil analysis system 800 as mentioned above and configured to perform calculations as the mobile soil analysis system 800 travels in the field. When the chemical sensor includes an integrated networking component, the processor may be physically or wirelessly connected to the chemical sensor. The processor may also be integrated with the controller of mobile soil analysis system 800 coupled to the mobility assembly, as discussed further below. Alternatively, the processor may be separate from mobile soil analysis system 800 and reside at a remote location. For example, the processor may be integrated into a central server or user computer and communicate with the chemical sensor over a communications network.
Fig. 11 illustrates an example process performed by a processor (such as an application or device controller) to control mobile soil analysis system 800 to determine soil element concentrations in soil samples in real-time. In some embodiments, in step 1102, the processor is configured to cause the mobility component to move. This may be in response to receiving instructions from a remote user computer or a user action to open a switch within mobile soil analysis system 800, for example. In step 1104, the processor is configured to detect the extraction device and the chemical sensor. The chemical sensor may signal its own operational status as well as the operational status of the extraction device to the processor, including confirming that a quantity of extractant solution is ready in the container of the extraction device. In step 1106, the processor is configured to perform steps 1108, 1110, 1112, and 1114 repeatedly and in real-time as soil samples are continuously collected. These steps may be performed, for example, every 6-10 seconds.
In some embodiments, in step 1108, the processor is configured to cause the soil detector to collect a soil sample. The processor may control the depth of detection (such as 6-12 inches), the amount of soil collected (such as 60 grams), and other parameters in the soil collection. The processor may also be configured to cause proper operation of the soil grinder, soil screen, or soil transporter to produce a soil sample ready to be mixed into the extractant solution before the next soil sample is collected. In step 1110, the processor is configured to cause the soil mixer to mix the soil sample into the extractant solution or solution mixture. The processor may control the position or speed of the mixer or the manner of mixing to extract as many target soil elements as possible in the shortest amount of time. In step 1112, the processor is configured to receive a reading from the chemical sensor of the cumulative concentration level of the target analyte in the solution mixture. The processor may store the reading for further processing. The processor may also be configured to determine whether the cumulative concentration level is within a normal range, and cause a report of an error or warning when the cumulative concentration level falls outside the normal range. For example, a warning may signal cleaning or replacement of the extraction device when too much soil has been added to the extractant solution, when mixing is unsuccessful, or when the container breaks. In step 1114, the processor is then configured to calculate a concentration level of the target analyte in the soil sample just added to the solution mixture. The processor may also be configured to calculate an amount of the target analyte to be added to the area where the soil sample is collected and send a recommendation for that amount. With these features, as the mobile soil analysis system 800 travels in a field, it can measure the amount of nitrate currently in a soil area and apply an appropriate amount of fertilizer to the soil area in real time. Further, the processor may be configured to receive additional data from the location sensor, or to send the calculated concentration level, the received location information, or the suggested amount of nutrients to add to a remote server or user computer, or to save it in local memory.
Claims (20)
1. A system for measuring the concentration of a soil element in an agricultural field as the system traverses the field, comprising:
a retrieval device coupled to a mobility component configured to move the system in the farm field,
the extraction device is configured to continuously receive a plurality of soil samples from a soil probe coupled to the mobility assembly while the mobility assembly is operating,
the extraction device comprises an extractant solution as a solvent for the soil sample; and is
The extraction device comprises a mixer configured to mix the soil sample with the extractant solution, thereby forming a solution mixture;
a chemical sensor coupled to the extraction device, the chemical sensor configured to measure a concentration level of a soil element in the solution mixture;
a processor coupled to the chemical sensor, the processor configured to calculate a concentration level of a soil element in each of the plurality of soil samples after the extraction device receives the soil sample and before the extraction device receives successive soil samples.
2. The apparatus of claim 1, the mixer comprising a paddle and an engine configured to rotate the paddle, the engine configured to rotate the paddle at least 10rpm to complete extraction of the soil element into the solution mixture in one second.
3. The apparatus of claim 1, the extraction device configured to receive at least 60 grams of soil sample at a speed of at least ten miles per hour, at least every 10 feet of travel.
4. The apparatus of claim 1, further comprising:
the mobility component;
the soil detector;
a controller configured to control operation of the mixer, the mobility assembly, or the soil prober.
5. The apparatus of claim 1, the extraction apparatus being detachable from the mobility assembly.
6. The apparatus of claim 1, the chemical sensor being an Ion Selective Electrode (ISE).
7. The apparatus of claim 1, wherein the first and second electrodes are disposed in a common plane,
the soil element is a nitrate salt, and the soil element is a nitrate,
the chemical sensor is substantially free of interference from chloride ions.
8. The apparatus of claim 1, the chemical sensor configured to detect a concentration of between at least 0.1 and 2,500ppm within 10 seconds with reproducibility within plus or minus 10% of full scale.
9. The device of claim 1, the extractant solution comprising an inhibitor chelator to reduce soil interference.
10. The apparatus of claim 1, further comprising:
a location sensor coupled to the mobility component, the location sensor configured to generate geographic coordinates for each of the plurality of soil samples,
the processor transmits the concentration level of the soil element in each of the plurality of soil samples to a remote server in association with the geographic coordinates generated for that soil sample.
11. A computer-implemented method of measuring soil constituent concentrations in a field, comprising:
detecting an extraction device coupled to a mobility assembly and a chemical sensor coupled to the extraction device, the extraction device comprising a mixer and a container containing an extractant solution; and
repeatedly performing the following operations during movement of the mobility component until a stop condition is reached:
causing the extraction device to receive a soil sample from a soil probe;
causing the mixer to mix the soil sample into the extractant solution, thereby forming a solution mixture;
receiving a reading from the chemical sensor of a concentration level of a target analyte in the solution mixture; and
calculating a concentration level of the target analyte in the soil sample from the readings.
12. The computer-implemented method of claim 11, further comprising: moving the mobility assembly at a speed of at least ten miles per hour.
13. The computer-implemented method of claim 11, the executing further comprising:
receiving geographic coordinates indicative of where the soil sample was collected;
transmitting the concentration level of the target analyte in the soil sample and the geographic coordinates to a remote server over a communications network.
14. The computer-implemented method of claim 11, the executing further comprising:
determining an amount of the target analyte to be added to the area where the soil sample is collected based on the concentration level;
so that the amount is displayed.
15. The computer-implemented method of claim 11, the performing being repeated every 6-10 seconds.
16. The computer-implemented method of claim 11, the stop condition being: a certain number of soil samples have been mixed into the extractant solution, a certain volume of soil has been mixed into the extractant solution, or the extractant solution has reached saturation.
17. The computer-implemented method of claim 11, further comprising: causing the extraction device to be updated or replaced when the stop condition is reached.
18. The computer-implemented method of claim 11, further comprising: rotating the mixer at least 10rpm to complete extraction of the soil element into the solution mixture in one second.
19. The computer-implemented method of claim 11, further comprising: the extraction device is caused to receive at least 60 grams of soil sample at a speed of at least ten miles per hour, at least every 10 feet of travel.
20. The computer-implemented method of claim 11, further comprising: the soil probe is caused to collect at least 60 grams of soil sample at a speed of at least ten miles per hour for at least every 10 feet of travel.
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| CN115791669A (en) * | 2023-02-10 | 2023-03-14 | 中国建设基础设施有限公司 | Method and system for determining fertilization proportion for soil remediation |
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| US10488363B2 (en) | 2017-10-03 | 2019-11-26 | The Climate Corporation | Field measurement of soil element concentration |
| CA3104255C (en) | 2018-07-10 | 2023-10-31 | Precision Planting Llc | Agricultural sampling system and related methods |
| US11768188B2 (en) * | 2018-10-24 | 2023-09-26 | Climate Llc | Cartridge-based sensor system for monitoring properties of field soils and wastewater |
| WO2020086601A1 (en) * | 2018-10-24 | 2020-04-30 | The Climate Corporation | In-ground sensor systems with modular sensors and wireless connectivity components |
| US20230088751A1 (en) * | 2019-08-09 | 2023-03-23 | Klonec Automation Systems Pvt. Ltd. | A soil analysis apparatus |
| US11483960B2 (en) * | 2019-11-19 | 2022-11-01 | Cnh Industrial Canada, Ltd. | System and method for monitoring seedbed conditions using a seedbed sensing assembly supported on a UAV |
| US20210278359A1 (en) * | 2020-03-05 | 2021-09-09 | Soiltech Wireless Inc | Environmental sensing device and application method thereof |
| KR102185273B1 (en) * | 2020-04-10 | 2020-12-01 | 국방과학연구소 | Method for detecting chemical contamination clouds and unmanned aerial vehicle performing method |
| US11860146B2 (en) | 2020-05-26 | 2024-01-02 | International Business Machines Corporation | Soil nutrient sensing platform |
| US11719681B2 (en) | 2020-10-30 | 2023-08-08 | International Business Machines Corporation | Capturing and analyzing data in a drone enabled environment for ecological decision making |
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| WO2024023729A1 (en) * | 2022-07-28 | 2024-02-01 | Precision Planting Llc | Agricultural sample packaging system and related methods |
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| EP3691435B1 (en) | 2024-05-01 |
| US10488363B2 (en) | 2019-11-26 |
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| AU2018345774B2 (en) | 2020-08-27 |
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| CN111417300B (en) | 2022-04-19 |
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